{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:KGV3YLOSXIB2ILZUPFWS7DQJM6","short_pith_number":"pith:KGV3YLOS","schema_version":"1.0","canonical_sha256":"51abbc2dd2ba03a42f34796d2f8e0967be56475684865f2e349daa0aef73d935","source":{"kind":"arxiv","id":"2409.09467","version":2},"attestation_state":"computed","paper":{"title":"Keeping Humans in the Loop: Human-Centered Automated Annotation with Generative AI","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.CL","authors_text":"Nicholas Pangakis, Samuel Wolken","submitted_at":"2024-09-14T15:27:43Z","abstract_excerpt":"Automated text annotation is a compelling use case for generative large language models (LLMs) in social media research. Recent work suggests that LLMs can achieve strong performance on annotation tasks; however, these studies evaluate LLMs on a small number of tasks and likely suffer from contamination due to a reliance on public benchmark datasets. Here, we test a human-centered framework for responsibly evaluating artificial intelligence tools used in automated annotation. We use GPT-4 to replicate 27 annotation tasks across 11 password-protected datasets from recently published computation"},"verification_status":{"content_addressed":true,"pith_receipt":true,"author_attested":false,"weak_author_claims":0,"strong_author_claims":0,"externally_anchored":false,"storage_verified":false,"citation_signatures":0,"replication_records":0,"graph_snapshot":true,"references_resolved":false,"formal_links_present":false},"canonical_record":{"source":{"id":"2409.09467","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2024-09-14T15:27:43Z","cross_cats_sorted":[],"title_canon_sha256":"955d55552a26038e269a357c5039613d561bb4411b8cb31ceeffe6e2029ba80e","abstract_canon_sha256":"7320d62dfae8e29ff5c400810fef9da1a0e12bc0f50ec1ad95cb47745df5b408"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T09:10:04.167633Z","signature_b64":"QfSVQ/7xo3Vy2DWKYUsDxttrEYYmhpFD28cz9VaoOfo/D4ZMvyF+w49YrAzDXnfcvdljOLeruYO4T2M0+CgUCA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"51abbc2dd2ba03a42f34796d2f8e0967be56475684865f2e349daa0aef73d935","last_reissued_at":"2026-07-05T09:10:04.167134Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T09:10:04.167134Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Keeping Humans in the Loop: Human-Centered Automated Annotation with Generative AI","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.CL","authors_text":"Nicholas Pangakis, Samuel Wolken","submitted_at":"2024-09-14T15:27:43Z","abstract_excerpt":"Automated text annotation is a compelling use case for generative large language models (LLMs) in social media research. Recent work suggests that LLMs can achieve strong performance on annotation tasks; however, these studies evaluate LLMs on a small number of tasks and likely suffer from contamination due to a reliance on public benchmark datasets. Here, we test a human-centered framework for responsibly evaluating artificial intelligence tools used in automated annotation. We use GPT-4 to replicate 27 annotation tasks across 11 password-protected datasets from recently published computation"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2409.09467","kind":"arxiv","version":2},"verdict":{"id":null,"model_set":{},"created_at":null,"strongest_claim":"","one_line_summary":"","pipeline_version":null,"weakest_assumption":"","pith_extraction_headline":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2409.09467/integrity.json","findings":[],"available":true,"detectors_run":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938"},"references":{"count":0,"sample":[],"resolved_work":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","internal_anchors":0},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"author_claims":{"count":0,"strong_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"builder_version":"pith-number-builder-2026-05-17-v1"},"aliases":[{"alias_kind":"arxiv","alias_value":"2409.09467","created_at":"2026-07-05T09:10:04.167194+00:00"},{"alias_kind":"arxiv_version","alias_value":"2409.09467v2","created_at":"2026-07-05T09:10:04.167194+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2409.09467","created_at":"2026-07-05T09:10:04.167194+00:00"},{"alias_kind":"pith_short_12","alias_value":"KGV3YLOSXIB2","created_at":"2026-07-05T09:10:04.167194+00:00"},{"alias_kind":"pith_short_16","alias_value":"KGV3YLOSXIB2ILZU","created_at":"2026-07-05T09:10:04.167194+00:00"},{"alias_kind":"pith_short_8","alias_value":"KGV3YLOS","created_at":"2026-07-05T09:10:04.167194+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2606.29393","citing_title":"The Role of Online Forums in Developer Understanding of Privacy Law -- A Reddit Case Study","ref_index":47,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/KGV3YLOSXIB2ILZUPFWS7DQJM6","json":"https://pith.science/pith/KGV3YLOSXIB2ILZUPFWS7DQJM6.json","graph_json":"https://pith.science/api/pith-number/KGV3YLOSXIB2ILZUPFWS7DQJM6/graph.json","events_json":"https://pith.science/api/pith-number/KGV3YLOSXIB2ILZUPFWS7DQJM6/events.json","paper":"https://pith.science/paper/KGV3YLOS"},"agent_actions":{"view_html":"https://pith.science/pith/KGV3YLOSXIB2ILZUPFWS7DQJM6","download_json":"https://pith.science/pith/KGV3YLOSXIB2ILZUPFWS7DQJM6.json","view_paper":"https://pith.science/paper/KGV3YLOS","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2409.09467&json=true","fetch_graph":"https://pith.science/api/pith-number/KGV3YLOSXIB2ILZUPFWS7DQJM6/graph.json","fetch_events":"https://pith.science/api/pith-number/KGV3YLOSXIB2ILZUPFWS7DQJM6/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/KGV3YLOSXIB2ILZUPFWS7DQJM6/action/timestamp_anchor","attest_storage":"https://pith.science/pith/KGV3YLOSXIB2ILZUPFWS7DQJM6/action/storage_attestation","attest_author":"https://pith.science/pith/KGV3YLOSXIB2ILZUPFWS7DQJM6/action/author_attestation","sign_citation":"https://pith.science/pith/KGV3YLOSXIB2ILZUPFWS7DQJM6/action/citation_signature","submit_replication":"https://pith.science/pith/KGV3YLOSXIB2ILZUPFWS7DQJM6/action/replication_record"}},"created_at":"2026-07-05T09:10:04.167194+00:00","updated_at":"2026-07-05T09:10:04.167194+00:00"}